{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:41.649676Z",
     "start_time": "2020-08-22T19:18:41.402018Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import os\n",
    "import sys\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:41.653730Z",
     "start_time": "2020-08-22T19:18:41.650647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.date(2010, 1, 1), datetime.date(2020, 8, 23))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set date timestamps for historical data download\n",
    "start_date = datetime.datetime(2010, 1, 1).date()\n",
    "end_date = datetime.datetime.now().date()\n",
    "start_date, end_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:41.660918Z",
     "start_time": "2020-08-22T19:18:41.654593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=3887)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_date - start_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:41.665451Z",
     "start_time": "2020-08-22T19:18:41.661982Z"
    }
   },
   "outputs": [],
   "source": [
    "ticker = \"GOOGL\"\n",
    "# ticker = \"MSFT\"\n",
    "# ticker = \"FB\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:41.674915Z",
     "start_time": "2020-08-22T19:18:41.666192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yfinance.Ticker object <GOOGL>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = yf.Ticker(ticker)\n",
    "\n",
    "msft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.400518Z",
     "start_time": "2020-08-22T19:18:41.675933Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zip': '94043',\n",
       " 'sector': 'Communication Services',\n",
       " 'fullTimeEmployees': 127498,\n",
       " 'longBusinessSummary': 'Alphabet Inc. provides online advertising services in the United States, Europe, the Middle East, Africa, the Asia-Pacific, Canada, and Latin America. It offers performance and brand advertising services. The company operates through Google and Other Bets segments. The Google segment offers products, such as Ads, Android, Chrome, Google Cloud, Google Maps, Google Play, Hardware, Search, and YouTube, as well as technical infrastructure. It also offers digital content, cloud services, hardware devices, and other miscellaneous products and services. The Other Bets segment includes businesses, including Access, Calico, CapitalG, GV, Verily, Waymo, and X, as well as Internet and television services. Alphabet Inc. was founded in 1998 and is headquartered in Mountain View, California.',\n",
       " 'city': 'Mountain View',\n",
       " 'phone': '650-253-0000',\n",
       " 'state': 'CA',\n",
       " 'country': 'United States',\n",
       " 'companyOfficers': [],\n",
       " 'website': 'http://www.abc.xyz',\n",
       " 'maxAge': 1,\n",
       " 'address1': '1600 Amphitheatre Parkway',\n",
       " 'industry': 'Internet Content & Information',\n",
       " 'previousClose': 1576.25,\n",
       " 'regularMarketOpen': 1571.8,\n",
       " 'twoHundredDayAverage': 1381.3533,\n",
       " 'trailingAnnualDividendYield': None,\n",
       " 'payoutRatio': 0,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 1591.88,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 1293262,\n",
       " 'totalAssets': None,\n",
       " 'regularMarketPreviousClose': 1576.25,\n",
       " 'fiftyDayAverage': 1518.2246,\n",
       " 'trailingAnnualDividendRate': None,\n",
       " 'open': 1571.8,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 1293262,\n",
       " 'expireDate': None,\n",
       " 'yield': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': None,\n",
       " 'exDividendDate': None,\n",
       " 'beta': 1.036263,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 1562.31,\n",
       " 'priceHint': 2,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 34.634,\n",
       " 'regularMarketVolume': 1742275,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 1073479942144,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 1754445,\n",
       " 'priceToSalesTrailing12Months': 6.465578,\n",
       " 'dayLow': 1562.31,\n",
       " 'ask': 1579.89,\n",
       " 'ytdReturn': None,\n",
       " 'askSize': 800,\n",
       " 'volume': 1742275,\n",
       " 'fiftyTwoWeekHigh': 1591.88,\n",
       " 'forwardPE': 27.871395,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': None,\n",
       " 'fiftyTwoWeekLow': 1008.87,\n",
       " 'bid': 1574.2,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': None,\n",
       " 'bidSize': 800,\n",
       " 'dayHigh': 1591.88,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Alphabet Inc.',\n",
       " 'longName': 'Alphabet Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EDT',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-14400000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'GOOGL',\n",
       " 'messageBoardId': 'finmb_29096',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 5.822,\n",
       " 'beta3Year': None,\n",
       " 'profitMargins': 0.18993,\n",
       " 'enterpriseToEbitda': 20.999,\n",
       " '52WeekChange': 0.3452841,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 56.53,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 300471008,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'bookValue': 304.342,\n",
       " 'sharesShort': 2609908,\n",
       " 'sharesPercentSharesOut': 0.0038,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1577750400,\n",
       " 'heldPercentInstitutions': 0.82294,\n",
       " 'netIncomeToCommon': 31534000128,\n",
       " 'trailingEps': 45.492,\n",
       " 'lastDividendValue': None,\n",
       " 'SandP52WeekChange': 0.18023336,\n",
       " 'priceToBook': 5.1769714,\n",
       " 'heldPercentInsiders': 0.0014300001,\n",
       " 'nextFiscalYearEnd': 1640908800,\n",
       " 'mostRecentQuarter': 1593475200,\n",
       " 'shortRatio': 1.39,\n",
       " 'sharesShortPreviousMonthDate': 1593475200,\n",
       " 'floatShares': 610528482,\n",
       " 'enterpriseValue': 966708428800,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 1396483200,\n",
       " 'lastSplitFactor': '1998:1000',\n",
       " 'legalType': None,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': -0.3,\n",
       " 'dateShortInterest': 1596153600,\n",
       " 'pegRatio': 7.42,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0087,\n",
       " 'sharesShortPriorMonth': 3094935,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'regularMarketPrice': 1571.8,\n",
       " 'logo_url': 'https://logo.clearbit.com/abc.xyz'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.725174Z",
     "start_time": "2020-08-22T19:18:43.401993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2679, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data = msft.history(start=start_date, end=end_date, interval=\"1d\").reset_index()\n",
    "\n",
    "historical_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.733900Z",
     "start_time": "2020-08-22T19:18:43.726741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>310.30</td>\n",
       "      <td>2437100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>313.69</td>\n",
       "      <td>3908400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>312.31</td>\n",
       "      <td>6003300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>313.24</td>\n",
       "      <td>313.24</td>\n",
       "      <td>303.48</td>\n",
       "      <td>304.43</td>\n",
       "      <td>7949400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>305.01</td>\n",
       "      <td>305.31</td>\n",
       "      <td>296.62</td>\n",
       "      <td>297.35</td>\n",
       "      <td>12815700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close    Volume  Dividends  \\\n",
       "0 2009-12-31  312.69  313.01  310.30  310.30   2437100          0   \n",
       "1 2010-01-04  313.79  315.07  312.43  313.69   3908400          0   \n",
       "2 2010-01-05  313.90  314.23  311.08  312.31   6003300          0   \n",
       "3 2010-01-06  313.24  313.24  303.48  304.43   7949400          0   \n",
       "4 2010-01-07  305.01  305.31  296.62  297.35  12815700          0   \n",
       "\n",
       "   Stock Splits  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data shows stock prices on per day basis.\n",
    "\n",
    "+ High: Max stock price on that particular day\n",
    "+ Low: Lowest price went on that particular day\n",
    "+ Open: Stock price opening on that particular day\n",
    "+ Close: Final closing price of the stock on that particular day\n",
    "+ Volume: Amount of stock traded on that day\n",
    "+ Dividends: Dividens given (if any)\n",
    "+ Stock Splits: Stock split happend on that particular day (if any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.760399Z",
     "start_time": "2020-08-22T19:18:43.735196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2679.000000</td>\n",
       "      <td>2679.000000</td>\n",
       "      <td>2679.000000</td>\n",
       "      <td>2679.000000</td>\n",
       "      <td>2.679000e+03</td>\n",
       "      <td>2679.0</td>\n",
       "      <td>2679.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>696.466558</td>\n",
       "      <td>702.655006</td>\n",
       "      <td>689.932647</td>\n",
       "      <td>696.549194</td>\n",
       "      <td>3.311138e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>364.876703</td>\n",
       "      <td>368.678913</td>\n",
       "      <td>361.285533</td>\n",
       "      <td>365.290921</td>\n",
       "      <td>2.626416e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>219.370000</td>\n",
       "      <td>221.360000</td>\n",
       "      <td>217.030000</td>\n",
       "      <td>218.250000</td>\n",
       "      <td>5.206000e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>332.840000</td>\n",
       "      <td>335.770000</td>\n",
       "      <td>330.240000</td>\n",
       "      <td>334.210000</td>\n",
       "      <td>1.545400e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>591.730000</td>\n",
       "      <td>595.300000</td>\n",
       "      <td>587.450000</td>\n",
       "      <td>591.970000</td>\n",
       "      <td>2.387600e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1036.195000</td>\n",
       "      <td>1050.525000</td>\n",
       "      <td>1023.580000</td>\n",
       "      <td>1036.455000</td>\n",
       "      <td>4.352950e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1585.030000</td>\n",
       "      <td>1591.880000</td>\n",
       "      <td>1562.310000</td>\n",
       "      <td>1576.250000</td>\n",
       "      <td>2.961990e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.998000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close        Volume  \\\n",
       "count  2679.000000  2679.000000  2679.000000  2679.000000  2.679000e+03   \n",
       "mean    696.466558   702.655006   689.932647   696.549194  3.311138e+06   \n",
       "std     364.876703   368.678913   361.285533   365.290921  2.626416e+06   \n",
       "min     219.370000   221.360000   217.030000   218.250000  5.206000e+05   \n",
       "25%     332.840000   335.770000   330.240000   334.210000  1.545400e+06   \n",
       "50%     591.730000   595.300000   587.450000   591.970000  2.387600e+06   \n",
       "75%    1036.195000  1050.525000  1023.580000  1036.455000  4.352950e+06   \n",
       "max    1585.030000  1591.880000  1562.310000  1576.250000  2.961990e+07   \n",
       "\n",
       "       Dividends  Stock Splits  \n",
       "count     2679.0   2679.000000  \n",
       "mean         0.0      0.000746  \n",
       "std          0.0      0.038602  \n",
       "min          0.0      0.000000  \n",
       "25%          0.0      0.000000  \n",
       "50%          0.0      0.000000  \n",
       "75%          0.0      0.000000  \n",
       "max          0.0      1.998000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.862681Z",
     "start_time": "2020-08-22T19:18:43.761396Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4268745970>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(historical_data.Close)\n",
    "\n",
    "plt.legend([\"Close\", \"Open\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.870180Z",
     "start_time": "2020-08-22T19:18:43.863644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>310.30</td>\n",
       "      <td>2437100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>313.69</td>\n",
       "      <td>3908400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>312.31</td>\n",
       "      <td>6003300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>313.24</td>\n",
       "      <td>313.24</td>\n",
       "      <td>303.48</td>\n",
       "      <td>304.43</td>\n",
       "      <td>7949400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>305.01</td>\n",
       "      <td>305.31</td>\n",
       "      <td>296.62</td>\n",
       "      <td>297.35</td>\n",
       "      <td>12815700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close    Volume  Dividends  \\\n",
       "0 2009-12-31  312.69  313.01  310.30  310.30   2437100          0   \n",
       "1 2010-01-04  313.79  315.07  312.43  313.69   3908400          0   \n",
       "2 2010-01-05  313.90  314.23  311.08  312.31   6003300          0   \n",
       "3 2010-01-06  313.24  313.24  303.48  304.43   7949400          0   \n",
       "4 2010-01-07  305.01  305.31  296.62  297.35  12815700          0   \n",
       "\n",
       "   Stock Splits  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.900222Z",
     "start_time": "2020-08-22T19:18:43.871042Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Close_lag_1</th>\n",
       "      <th>Open_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>High_lag_4</th>\n",
       "      <th>Low_lag_4</th>\n",
       "      <th>Close_lag_5</th>\n",
       "      <th>Open_lag_5</th>\n",
       "      <th>High_lag_5</th>\n",
       "      <th>Low_lag_5</th>\n",
       "      <th>Close_lag_6</th>\n",
       "      <th>Open_lag_6</th>\n",
       "      <th>High_lag_6</th>\n",
       "      <th>Low_lag_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>310.30</td>\n",
       "      <td>2437100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>313.69</td>\n",
       "      <td>3908400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.30</td>\n",
       "      <td>312.69</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>312.31</td>\n",
       "      <td>6003300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>313.69</td>\n",
       "      <td>313.79</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>313.24</td>\n",
       "      <td>313.24</td>\n",
       "      <td>303.48</td>\n",
       "      <td>304.43</td>\n",
       "      <td>7949400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>312.31</td>\n",
       "      <td>313.90</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>305.01</td>\n",
       "      <td>305.31</td>\n",
       "      <td>296.62</td>\n",
       "      <td>297.35</td>\n",
       "      <td>12815700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.43</td>\n",
       "      <td>313.24</td>\n",
       "      <td>...</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close    Volume  Dividends  \\\n",
       "0 2009-12-31  312.69  313.01  310.30  310.30   2437100          0   \n",
       "1 2010-01-04  313.79  315.07  312.43  313.69   3908400          0   \n",
       "2 2010-01-05  313.90  314.23  311.08  312.31   6003300          0   \n",
       "3 2010-01-06  313.24  313.24  303.48  304.43   7949400          0   \n",
       "4 2010-01-07  305.01  305.31  296.62  297.35  12815700          0   \n",
       "\n",
       "   Stock Splits  Close_lag_1  Open_lag_1  ...  High_lag_4  Low_lag_4  \\\n",
       "0           0.0          NaN         NaN  ...         NaN        NaN   \n",
       "1           0.0       310.30      312.69  ...         NaN        NaN   \n",
       "2           0.0       313.69      313.79  ...         NaN        NaN   \n",
       "3           0.0       312.31      313.90  ...         NaN        NaN   \n",
       "4           0.0       304.43      313.24  ...      313.01      310.3   \n",
       "\n",
       "   Close_lag_5  Open_lag_5  High_lag_5  Low_lag_5  Close_lag_6  Open_lag_6  \\\n",
       "0          NaN         NaN         NaN        NaN          NaN         NaN   \n",
       "1          NaN         NaN         NaN        NaN          NaN         NaN   \n",
       "2          NaN         NaN         NaN        NaN          NaN         NaN   \n",
       "3          NaN         NaN         NaN        NaN          NaN         NaN   \n",
       "4          NaN         NaN         NaN        NaN          NaN         NaN   \n",
       "\n",
       "   High_lag_6  Low_lag_6  \n",
       "0         NaN        NaN  \n",
       "1         NaN        NaN  \n",
       "2         NaN        NaN  \n",
       "3         NaN        NaN  \n",
       "4         NaN        NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create lag features\n",
    "for i in range(1, 7):\n",
    "    historical_data[f\"Close_lag_{i}\"] = historical_data.Close.shift(periods=i, axis=0)\n",
    "    historical_data[f\"Open_lag_{i}\"] = historical_data.Open.shift(periods=i, axis=0)\n",
    "    historical_data[f\"High_lag_{i}\"] = historical_data.High.shift(periods=i, axis=0)\n",
    "    historical_data[f\"Low_lag_{i}\"] = historical_data.Low.shift(periods=i, axis=0)\n",
    "\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.915494Z",
     "start_time": "2020-08-22T19:18:43.901135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Close_lag_1</th>\n",
       "      <th>Open_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>High_lag_4</th>\n",
       "      <th>Low_lag_4</th>\n",
       "      <th>Close_lag_5</th>\n",
       "      <th>Open_lag_5</th>\n",
       "      <th>High_lag_5</th>\n",
       "      <th>Low_lag_5</th>\n",
       "      <th>Close_lag_6</th>\n",
       "      <th>Open_lag_6</th>\n",
       "      <th>High_lag_6</th>\n",
       "      <th>Low_lag_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>310.30</td>\n",
       "      <td>2437100</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>313.69</td>\n",
       "      <td>3908400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.30</td>\n",
       "      <td>312.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>312.31</td>\n",
       "      <td>6003300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>313.69</td>\n",
       "      <td>313.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>313.24</td>\n",
       "      <td>313.24</td>\n",
       "      <td>303.48</td>\n",
       "      <td>304.43</td>\n",
       "      <td>7949400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>312.31</td>\n",
       "      <td>313.90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>305.01</td>\n",
       "      <td>305.31</td>\n",
       "      <td>296.62</td>\n",
       "      <td>297.35</td>\n",
       "      <td>12815700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.43</td>\n",
       "      <td>313.24</td>\n",
       "      <td>...</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close    Volume  Dividends  \\\n",
       "0 2009-12-31  312.69  313.01  310.30  310.30   2437100          0   \n",
       "1 2010-01-04  313.79  315.07  312.43  313.69   3908400          0   \n",
       "2 2010-01-05  313.90  314.23  311.08  312.31   6003300          0   \n",
       "3 2010-01-06  313.24  313.24  303.48  304.43   7949400          0   \n",
       "4 2010-01-07  305.01  305.31  296.62  297.35  12815700          0   \n",
       "\n",
       "   Stock Splits  Close_lag_1  Open_lag_1  ...  High_lag_4  Low_lag_4  \\\n",
       "0           0.0         0.00        0.00  ...        0.00        0.0   \n",
       "1           0.0       310.30      312.69  ...        0.00        0.0   \n",
       "2           0.0       313.69      313.79  ...        0.00        0.0   \n",
       "3           0.0       312.31      313.90  ...        0.00        0.0   \n",
       "4           0.0       304.43      313.24  ...      313.01      310.3   \n",
       "\n",
       "   Close_lag_5  Open_lag_5  High_lag_5  Low_lag_5  Close_lag_6  Open_lag_6  \\\n",
       "0          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "1          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "2          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "3          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "4          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "\n",
       "   High_lag_6  Low_lag_6  \n",
       "0         0.0        0.0  \n",
       "1         0.0        0.0  \n",
       "2         0.0        0.0  \n",
       "3         0.0        0.0  \n",
       "4         0.0        0.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data.fillna(0, inplace=True)\n",
    "historical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.919277Z",
     "start_time": "2020-08-22T19:18:43.916431Z"
    }
   },
   "outputs": [],
   "source": [
    "historical_data.drop(\n",
    "    columns=[\"Dividends\", \"Stock Splits\", \"Volume\", \"Open\", \"High\", \"Low\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.923301Z",
     "start_time": "2020-08-22T19:18:43.920145Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Create a holiday dataframe\n",
    "# min_date, max_date = historical_data.Date.min(), historical_data.Date.max()\n",
    "# print(\"Min/Max dates:\", min_date, max_date)\n",
    "\n",
    "# date_range = pd.date_range(start=min_date, end=max_date)\n",
    "# print(\"Date range:\", date_range.min(), date_range.max())\n",
    "\n",
    "# # Find dates not present in historical df - closed stock market holiday\n",
    "# holiday_date_range = [d.date() for d in date_range if d not in historical_data.Date]\n",
    "# holiday_date_range[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:43.928182Z",
     "start_time": "2020-08-22T19:18:43.924111Z"
    }
   },
   "outputs": [],
   "source": [
    "# holidays = pd.DataFrame({\n",
    "#     \"holiday\": \"shutdown\",\n",
    "#     \"ds\": pd.to_datetime(holiday_date_range),\n",
    "#     \"lower_bound\": 0,\n",
    "#     \"upper_bound\": 1,\n",
    "# })\n",
    "\n",
    "# holidays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facebook's Prophet: Single Timestep Forecasting\n",
    "\n",
    "References: \n",
    "+ https://facebook.github.io/prophet/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:44.043035Z",
     "start_time": "2020-08-22T19:18:43.929346Z"
    }
   },
   "outputs": [],
   "source": [
    "import fbprophet as prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:44.046863Z",
     "start_time": "2020-08-22T19:18:44.044035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2649, 26), (30, 26))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_period = 30 # 30 days\n",
    "\n",
    "train, validation = historical_data.iloc[:-validation_period], historical_data.iloc[-validation_period:]\n",
    "\n",
    "train.shape, validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:44.070817Z",
     "start_time": "2020-08-22T19:18:44.047673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Close_lag_1</th>\n",
       "      <th>Open_lag_1</th>\n",
       "      <th>High_lag_1</th>\n",
       "      <th>Low_lag_1</th>\n",
       "      <th>Close_lag_2</th>\n",
       "      <th>Open_lag_2</th>\n",
       "      <th>High_lag_2</th>\n",
       "      <th>Low_lag_2</th>\n",
       "      <th>...</th>\n",
       "      <th>High_lag_4</th>\n",
       "      <th>Low_lag_4</th>\n",
       "      <th>Close_lag_5</th>\n",
       "      <th>Open_lag_5</th>\n",
       "      <th>High_lag_5</th>\n",
       "      <th>Low_lag_5</th>\n",
       "      <th>Close_lag_6</th>\n",
       "      <th>Open_lag_6</th>\n",
       "      <th>High_lag_6</th>\n",
       "      <th>Low_lag_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>310.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>313.69</td>\n",
       "      <td>310.30</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>312.31</td>\n",
       "      <td>313.69</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>310.30</td>\n",
       "      <td>312.69</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>304.43</td>\n",
       "      <td>312.31</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>313.69</td>\n",
       "      <td>313.79</td>\n",
       "      <td>315.07</td>\n",
       "      <td>312.43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>297.35</td>\n",
       "      <td>304.43</td>\n",
       "      <td>313.24</td>\n",
       "      <td>313.24</td>\n",
       "      <td>303.48</td>\n",
       "      <td>312.31</td>\n",
       "      <td>313.90</td>\n",
       "      <td>314.23</td>\n",
       "      <td>311.08</td>\n",
       "      <td>...</td>\n",
       "      <td>313.01</td>\n",
       "      <td>310.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Close  Close_lag_1  Open_lag_1  High_lag_1  Low_lag_1  \\\n",
       "0 2009-12-31  310.30         0.00        0.00        0.00       0.00   \n",
       "1 2010-01-04  313.69       310.30      312.69      313.01     310.30   \n",
       "2 2010-01-05  312.31       313.69      313.79      315.07     312.43   \n",
       "3 2010-01-06  304.43       312.31      313.90      314.23     311.08   \n",
       "4 2010-01-07  297.35       304.43      313.24      313.24     303.48   \n",
       "\n",
       "   Close_lag_2  Open_lag_2  High_lag_2  Low_lag_2  ...  High_lag_4  Low_lag_4  \\\n",
       "0         0.00        0.00        0.00       0.00  ...        0.00        0.0   \n",
       "1         0.00        0.00        0.00       0.00  ...        0.00        0.0   \n",
       "2       310.30      312.69      313.01     310.30  ...        0.00        0.0   \n",
       "3       313.69      313.79      315.07     312.43  ...        0.00        0.0   \n",
       "4       312.31      313.90      314.23     311.08  ...      313.01      310.3   \n",
       "\n",
       "   Close_lag_5  Open_lag_5  High_lag_5  Low_lag_5  Close_lag_6  Open_lag_6  \\\n",
       "0          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "1          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "2          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "3          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "4          0.0         0.0         0.0        0.0          0.0         0.0   \n",
       "\n",
       "   High_lag_6  Low_lag_6  \n",
       "0         0.0        0.0  \n",
       "1         0.0        0.0  \n",
       "2         0.0        0.0  \n",
       "3         0.0        0.0  \n",
       "4         0.0        0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:44.073378Z",
     "start_time": "2020-08-22T19:18:44.071711Z"
    }
   },
   "outputs": [],
   "source": [
    "lag_features = [col for col in historical_data.columns if \"lag\" in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:47.184719Z",
     "start_time": "2020-08-22T19:18:44.074146Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f42685d36d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = prophet.Prophet(yearly_seasonality=True, weekly_seasonality=True, seasonality_mode=\"additive\")\n",
    "for name in lag_features:\n",
    "    model.add_regressor(name)\n",
    "\n",
    "model.fit(df=train.rename(columns={\"Date\": \"ds\", \"Close\":\"y\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:48.058259Z",
     "start_time": "2020-08-22T19:18:47.185795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 94)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model.predict(validation[[col for col in validation.columns if col != \"Close\"]].rename(columns={\"Date\": \"ds\"}))\n",
    "\n",
    "forecast.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:48.075028Z",
     "start_time": "2020-08-22T19:18:48.060708Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>Close_lag_1</th>\n",
       "      <th>Close_lag_1_lower</th>\n",
       "      <th>Close_lag_1_upper</th>\n",
       "      <th>Close_lag_2</th>\n",
       "      <th>...</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>656.779315</td>\n",
       "      <td>1511.242487</td>\n",
       "      <td>1547.604374</td>\n",
       "      <td>656.779315</td>\n",
       "      <td>656.779315</td>\n",
       "      <td>352.009840</td>\n",
       "      <td>352.009840</td>\n",
       "      <td>352.009840</td>\n",
       "      <td>27.827100</td>\n",
       "      <td>...</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>3.901252</td>\n",
       "      <td>3.901252</td>\n",
       "      <td>3.901252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1530.326162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>656.797009</td>\n",
       "      <td>1503.525395</td>\n",
       "      <td>1539.139361</td>\n",
       "      <td>656.797009</td>\n",
       "      <td>656.797009</td>\n",
       "      <td>340.950198</td>\n",
       "      <td>340.950198</td>\n",
       "      <td>340.950198</td>\n",
       "      <td>28.507248</td>\n",
       "      <td>...</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>3.966537</td>\n",
       "      <td>3.966537</td>\n",
       "      <td>3.966537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1521.057190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>656.814703</td>\n",
       "      <td>1496.008974</td>\n",
       "      <td>1530.132024</td>\n",
       "      <td>656.814703</td>\n",
       "      <td>656.814703</td>\n",
       "      <td>344.514228</td>\n",
       "      <td>344.514228</td>\n",
       "      <td>344.514228</td>\n",
       "      <td>27.612193</td>\n",
       "      <td>...</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>3.989946</td>\n",
       "      <td>3.989946</td>\n",
       "      <td>3.989946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1512.781479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>656.832397</td>\n",
       "      <td>1489.697516</td>\n",
       "      <td>1524.211775</td>\n",
       "      <td>656.832397</td>\n",
       "      <td>656.832397</td>\n",
       "      <td>342.870562</td>\n",
       "      <td>342.870562</td>\n",
       "      <td>342.870562</td>\n",
       "      <td>27.900630</td>\n",
       "      <td>...</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>3.971896</td>\n",
       "      <td>3.971896</td>\n",
       "      <td>3.971896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1506.660234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>656.850091</td>\n",
       "      <td>1493.683956</td>\n",
       "      <td>1530.441626</td>\n",
       "      <td>656.850091</td>\n",
       "      <td>656.850091</td>\n",
       "      <td>342.061118</td>\n",
       "      <td>342.061118</td>\n",
       "      <td>342.061118</td>\n",
       "      <td>27.767608</td>\n",
       "      <td>...</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>3.913400</td>\n",
       "      <td>3.913400</td>\n",
       "      <td>3.913400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1512.045898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-20</td>\n",
       "      <td>656.903173</td>\n",
       "      <td>1489.616457</td>\n",
       "      <td>1524.465581</td>\n",
       "      <td>656.903173</td>\n",
       "      <td>656.903173</td>\n",
       "      <td>342.858172</td>\n",
       "      <td>342.858172</td>\n",
       "      <td>342.858172</td>\n",
       "      <td>27.702100</td>\n",
       "      <td>...</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>59.971018</td>\n",
       "      <td>3.513434</td>\n",
       "      <td>3.513434</td>\n",
       "      <td>3.513434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1506.296044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-21</td>\n",
       "      <td>656.920867</td>\n",
       "      <td>1544.570638</td>\n",
       "      <td>1579.755549</td>\n",
       "      <td>656.920867</td>\n",
       "      <td>656.920867</td>\n",
       "      <td>362.264168</td>\n",
       "      <td>362.264168</td>\n",
       "      <td>362.264168</td>\n",
       "      <td>27.766605</td>\n",
       "      <td>...</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>60.571234</td>\n",
       "      <td>3.313684</td>\n",
       "      <td>3.313684</td>\n",
       "      <td>3.313684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561.411803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-22</td>\n",
       "      <td>656.938561</td>\n",
       "      <td>1524.909497</td>\n",
       "      <td>1558.259818</td>\n",
       "      <td>656.938561</td>\n",
       "      <td>656.938561</td>\n",
       "      <td>358.993355</td>\n",
       "      <td>358.993355</td>\n",
       "      <td>358.993355</td>\n",
       "      <td>29.337129</td>\n",
       "      <td>...</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>61.040568</td>\n",
       "      <td>3.085870</td>\n",
       "      <td>3.085870</td>\n",
       "      <td>3.085870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1541.718203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>656.956255</td>\n",
       "      <td>1544.839739</td>\n",
       "      <td>1581.676478</td>\n",
       "      <td>656.956255</td>\n",
       "      <td>656.956255</td>\n",
       "      <td>362.681279</td>\n",
       "      <td>362.681279</td>\n",
       "      <td>362.681279</td>\n",
       "      <td>29.072422</td>\n",
       "      <td>...</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>60.624196</td>\n",
       "      <td>2.833519</td>\n",
       "      <td>2.833519</td>\n",
       "      <td>2.833519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1563.552263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>656.973949</td>\n",
       "      <td>1491.046076</td>\n",
       "      <td>1526.073524</td>\n",
       "      <td>656.973949</td>\n",
       "      <td>656.973949</td>\n",
       "      <td>342.816874</td>\n",
       "      <td>342.816874</td>\n",
       "      <td>342.816874</td>\n",
       "      <td>29.370885</td>\n",
       "      <td>...</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>60.080551</td>\n",
       "      <td>2.560343</td>\n",
       "      <td>2.560343</td>\n",
       "      <td>2.560343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1508.609393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds       trend   yhat_lower   yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2020-07-13  656.779315  1511.242487  1547.604374   656.779315   656.779315   \n",
       "1 2020-07-14  656.797009  1503.525395  1539.139361   656.797009   656.797009   \n",
       "2 2020-07-15  656.814703  1496.008974  1530.132024   656.814703   656.814703   \n",
       "3 2020-07-16  656.832397  1489.697516  1524.211775   656.832397   656.832397   \n",
       "4 2020-07-17  656.850091  1493.683956  1530.441626   656.850091   656.850091   \n",
       "5 2020-07-20  656.903173  1489.616457  1524.465581   656.903173   656.903173   \n",
       "6 2020-07-21  656.920867  1544.570638  1579.755549   656.920867   656.920867   \n",
       "7 2020-07-22  656.938561  1524.909497  1558.259818   656.938561   656.938561   \n",
       "8 2020-07-23  656.956255  1544.839739  1581.676478   656.956255   656.956255   \n",
       "9 2020-07-24  656.973949  1491.046076  1526.073524   656.973949   656.973949   \n",
       "\n",
       "   Close_lag_1  Close_lag_1_lower  Close_lag_1_upper  Close_lag_2  ...  \\\n",
       "0   352.009840         352.009840         352.009840    27.827100  ...   \n",
       "1   340.950198         340.950198         340.950198    28.507248  ...   \n",
       "2   344.514228         344.514228         344.514228    27.612193  ...   \n",
       "3   342.870562         342.870562         342.870562    27.900630  ...   \n",
       "4   342.061118         342.061118         342.061118    27.767608  ...   \n",
       "5   342.858172         342.858172         342.858172    27.702100  ...   \n",
       "6   362.264168         362.264168         362.264168    27.766605  ...   \n",
       "7   358.993355         358.993355         358.993355    29.337129  ...   \n",
       "8   362.681279         362.681279         362.681279    29.072422  ...   \n",
       "9   342.816874         342.816874         342.816874    29.370885  ...   \n",
       "\n",
       "      weekly  weekly_lower  weekly_upper    yearly  yearly_lower  \\\n",
       "0  59.971018     59.971018     59.971018  3.901252      3.901252   \n",
       "1  60.571234     60.571234     60.571234  3.966537      3.966537   \n",
       "2  61.040568     61.040568     61.040568  3.989946      3.989946   \n",
       "3  60.624196     60.624196     60.624196  3.971896      3.971896   \n",
       "4  60.080551     60.080551     60.080551  3.913400      3.913400   \n",
       "5  59.971018     59.971018     59.971018  3.513434      3.513434   \n",
       "6  60.571234     60.571234     60.571234  3.313684      3.313684   \n",
       "7  61.040568     61.040568     61.040568  3.085870      3.085870   \n",
       "8  60.624196     60.624196     60.624196  2.833519      2.833519   \n",
       "9  60.080551     60.080551     60.080551  2.560343      2.560343   \n",
       "\n",
       "   yearly_upper  multiplicative_terms  multiplicative_terms_lower  \\\n",
       "0      3.901252                   0.0                         0.0   \n",
       "1      3.966537                   0.0                         0.0   \n",
       "2      3.989946                   0.0                         0.0   \n",
       "3      3.971896                   0.0                         0.0   \n",
       "4      3.913400                   0.0                         0.0   \n",
       "5      3.513434                   0.0                         0.0   \n",
       "6      3.313684                   0.0                         0.0   \n",
       "7      3.085870                   0.0                         0.0   \n",
       "8      2.833519                   0.0                         0.0   \n",
       "9      2.560343                   0.0                         0.0   \n",
       "\n",
       "   multiplicative_terms_upper         yhat  \n",
       "0                         0.0  1530.326162  \n",
       "1                         0.0  1521.057190  \n",
       "2                         0.0  1512.781479  \n",
       "3                         0.0  1506.660234  \n",
       "4                         0.0  1512.045898  \n",
       "5                         0.0  1506.296044  \n",
       "6                         0.0  1561.411803  \n",
       "7                         0.0  1541.718203  \n",
       "8                         0.0  1563.552263  \n",
       "9                         0.0  1508.609393  \n",
       "\n",
       "[10 rows x 94 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:48.215672Z",
     "start_time": "2020-08-22T19:18:48.076633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting predictions and uncertainity interval with respect to actuals\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot actuals\n",
    "plt.plot(historical_data.Date.iloc[-365:], historical_data.Close.iloc[-365:])\n",
    "\n",
    "# Plot forecasts for the latest 365 days as validation period\n",
    "plt.plot(forecast.ds, forecast.yhat)\n",
    "\n",
    "# Plot uncertainity - lower and upper bound for the forecasts\n",
    "plt.fill_between(forecast.ds, forecast.yhat_lower, forecast.yhat_upper, alpha=0.2)\n",
    "\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Predicted `Close` price of Stock\")\n",
    "plt.legend([\"Actual\", \"Forecast\", \"Forecast Uncertainity\"])\n",
    "plt.title(f\"{ticker}_Model_Forecast_Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:49.008773Z",
     "start_time": "2020-08-22T19:18:48.216537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = model.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:49.038971Z",
     "start_time": "2020-08-22T19:18:49.010104Z"
    }
   },
   "outputs": [],
   "source": [
    "# General metrics\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# Mean absolute error metric\n",
    "def mae(y, yhat):\n",
    "    return mean_absolute_error(y, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:49.043119Z",
     "start_time": "2020-08-22T19:18:49.040045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2020-07-13 00:00:00'), Timestamp('2020-08-21 00:00:00'))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.Date.min(), validation.Date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:49.051021Z",
     "start_time": "2020-08-22T19:18:49.043959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2020-07-13 00:00:00'), Timestamp('2020-08-21 00:00:00'))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.ds.min(), forecast.ds.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-22T19:18:49.057554Z",
     "start_time": "2020-08-22T19:18:49.052358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Mean absolute error (Closing price of the stock) = ', 17.75032078404112)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Mean absolute error (Closing price of the stock) = \", mae(validation.Close, forecast.yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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